Chapter 1, A Gentle Primer on Big Data, covers the basic concepts of big data and machine learning and the tools used, and gives a general understanding of what big data analytics pertains to.
Chapter 2, Getting started with Big Data Mining, introduces concepts of big data mining in an enterprise and provides an introduction to the software and hardware architecture stack for enterprise big data.
Chapter 3, The Analytics Toolkit, discusses the various tools used for big data and machine Learning and provides step-by-step instructions on where users can download and install tools such as R, Python, and Hadoop.Â
Chapter 4, Big Data with Hadoop, looks at the fundamental concepts of Hadoop and delves into the detailed technical aspects of the Hadoop ecosystem. Core components of Hadoop such as Hadoop Distributed File System (HDFS), Hadoop Yarn, Hadoop MapReduce and concepts in Hadoop 2 such as ResourceManager, NodeManger, Application Master have been explained in this chapter. A step-by-step tutorial on using Hive via the Cloudera Distribution of Hadoop (CDH) has also been included in the chapter.
Chapter 5, Big Data Analytics with NoSQL, looks at the various emerging and unique database solutions popularly known as NoSQL, which has upended the traditional model of relational databases. We will discuss the core concepts and technical aspects of NoSQL. The various types of NoSQL systems such as In-Memory, Columnar, Document-based, Key-Value, Graph and others have been covered in this section. A tutorial related to MongoDB and the MongoDB Compass interface as well as an extremely comprehensive tutorial on creating a production-grade R Shiny Dashboard with kdb+ have been included.
Chapter 6, Spark for Big Data Analytics, looks at how to use Spark for big data analytics. Both high-level concepts as well as technical topics have been covered. Key concepts such as SparkContext, Directed Acyclic Graphs, Actions & Transformations have been covered. There is also a complete tutorial on using Spark on Databricks, a platform via which users can leverage Spark
Chapter 7, A Gentle Introduction to Machine Learning Concepts, speaks about the fundamental concepts in machine learning. Further, core concepts such as supervised vs unsupervised learning, classification, regression, feature engineering, data preprocessing and cross-validation have been discussed. The chapter ends with a brief tutorial on using an R library for Neural Networks.
Chapter 8, Machine Learning Deep Dive, delves into some of the more involved aspects of machine learning. Algorithms, bias, variance, regularization, and various other concepts in Machine Learning have been discussed in depth. The chapter also includes explanations of algorithms such as random forest, support vector machines, decision trees. The chapter ends with a comprehensive tutorial on creating a web-based machine learning application.
Chapter 9, Enterprise Data Science, discusses the technical considerations for deploying enterprise-scale data science and big data solutions. We will also discuss the various ways enterprises across the world are implementing their big data strategies, including cloud-based solutions. A step-by-step tutorial on using AWS - Amazon Web Services has also been provided in the chapter.
Chapter 10, Closing Thoughts on Big Data, discusses corporate big data and Data Science strategies and concludes with some pointers on how to make big data related projects successful.
Appendix A, Further Reading on Big Data, contains links for a wider understanding of big data.
